机器学习驱动的机器人身体形态的自我发现
Fernando Díaz Ledezma1, Sami Haddadin1
1Chair of Robotics and Systems Intelligence, MIRMI-Munich Institute of Robotics and Machine Intelligence, Technical University of Munich, Georg-Brauchle-Ring 60-62, München 80992, Germany.
Science robotics
|December 13, 2023
概括
机器人现在可以学习自己的物理结构,包括关节位置和方向,仅使用内部传感器数据. 这种新的方法,称为自感信息图,绕过了对外测量设备的需求,以进行形态发现.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 传统的机器人形态被认为是已知的,外部传感器用于校准.
- 自主学习机器人形态仍然是一个未被充分探索的领域.
研究的目的:
- 为了调查机器人是否可以只使用自身感知信号来学习自己的形态.
- 开发一种从内部传感器数据推断机器人形态的方法.
主要方法:
- 提出了一种基于信息的相互表征,称为自感信息图 (π-图).
- 在π图中对对分析信号关系,以确定动力学原理.
- 推断机械拓和动力学描述 (关节位置/方向).
主要成果:
- 从多种机器人系统 (操纵器,六足动物,人形) 的π图表中成功推断出机器人形态.
- 证明了线下和线上有效的推断,独立于机器人的复杂性.
- 验证了对形态自我发现的以代理为中心的方法.
结论:
- 自主感知信息图使机器人能够自主学习其形态.
- 这种方法减少了对机器人校准和理解的外部测量设备的依赖.
- 这些发现为适应性和自我意识的机器人系统开辟了新的途径.
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